Automatic segmentation, feature extraction and comparison of healthy and stroke cerebral vasculature

Deshpande, Aditi; Jamilpour, Nima; Jiang, Bin; Michel, Patrik; Eskandari, Ashraf; Kidwell, Chelsea S.; Laksari, Kaveh

doi:10.1016/j.nicl.2021.102573

Cited by 37 publications

(47 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A vascular atlas with geometric features can be used to study and quantify the variations in vascular geometry within the healthy population. As shown by our previous study, vascular morphology can be characterized using geometric properties of the vessel network, such as tortuosity, fractality (quantified using fractal dimensions), branching pattern, average diameter, total length, and volume 19 . These properties have been shown to be corroborated indicators of vascular health and potential pathology 8,19–21 .…”

Section: Introductionmentioning

confidence: 86%

“…As shown by our previous study, vascular morphology can be characterized using geometric properties of the vessel network, such as tortuosity, fractality (quantified using fractal dimensions), branching pattern, average diameter, total length, and volume 19 . These properties have been shown to be corroborated indicators of vascular health and potential pathology 8,19–21 . Most brain atlases typically do not include detailed morphology of the brain vascular network due to inadequate vascular imaging data from a large sample of healthy subjects 22 and a lack of validated algorithms to segment, extract, and analyze cerebral vasculature.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Novel imaging markers for altered cerebrovascular morphology in aging, stroke, and Alzheimer's disease

Deshpande

Elliott

Kari

et al. 2022

Journal of Neuroimaging

Self Cite

View full text Add to dashboard Cite

Background and Purpose: Altered brain vasculature is a key phenomenon in several neurologic disorders. This paper presents a quantitative assessment of the anatomical variations in the Circle of Willis (CoW) and vascular morphology in healthy aging, acute ischemic stroke (AIS) and Alzheimer's Disease (AD). Methods:We used our novel automatic method to segment and extract geometric features of the cerebral vasculature from MR angiography scans of 175 healthy subjects, which were used to create a probabilistic atlas of cerebrovasculature and to study normal aging and intersubject variations in CoW anatomy. Subsequently, we quantified and analyzed vascular alterations in 45AIS and 50 AD patients, two prominent cerebrovascular and neurodegenerative disorders. Results:In the sampled cohort, we determined that the CoW is fully formed in only 35% of healthy adults and found significantly (p < .05) increased tortuosity and fractality, with increasing age and also with disease in both AIS and AD. We also found significantly lower vessel length, volume, and number of branches in AIS patients, as expected. The AD cerebral vessels exhibited significantly smaller diameter and more complex branching patterns, compared to age-matched healthy adults. These changes were significantly heightened (p < .05) among healthy, early onset mild AD, and moderate/severe dementia groups. Conclusion:Although our study does not include longitudinal data due to paucity of such datasets, the specific geometric features and quantitative comparisons demonstrate the potential for using vascular morphology as a noninvasive imaging biomarker for neurologic disorders.

show abstract

Section: Introductionmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Novel imaging markers for altered cerebrovascular morphology in aging, stroke, and Alzheimer's disease

Deshpande

Elliott

Kari

et al. 2022

Journal of Neuroimaging

Self Cite

View full text Add to dashboard Cite

show abstract

“…These errors were regularly encountered in segmentations produced by state of the art deep learning models [ 5 , 8 ] and also other traditional methods like region growing or graph cut algorithms [ 8 ]. Additionally, these errors are also encountered in the literature [ 21 – 25 ]. The errors included, for example, boundary errors of various vessel segments, false positively labelled anatomical vessel and non-vessel structures such as the sagittal sinus, middle meningeal artery, fat and muscle tissue and omitted parts of the vessel tree.…”

Section: Methodsmentioning

confidence: 99%

An evaluation of performance measures for arterial brain vessel segmentation

et al. 2021

View full text Add to dashboard Cite

Background Arterial brain vessel segmentation allows utilising clinically relevant information contained within the cerebral vascular tree. Currently, however, no standardised performance measure is available to evaluate the quality of cerebral vessel segmentations. Thus, we developed a performance measure selection framework based on manual visual scoring of simulated segmentation variations to find the most suitable measure for cerebral vessel segmentation. Methods To simulate segmentation variations, we manually created non-overlapping segmentation errors common in magnetic resonance angiography cerebral vessel segmentation. In 10 patients, we generated a set of approximately 300 simulated segmentation variations for each ground truth image. Each segmentation was visually scored based on a predefined scoring system and segmentations were ranked based on 22 performance measures common in the literature. The correlation of visual scores with performance measure rankings was calculated using the Spearman correlation coefficient. Results The distance-based performance measures balanced average Hausdorff distance (rank = 1) and average Hausdorff distance (rank = 2) provided the segmentation rankings with the highest average correlation with manual rankings. They were followed by overlap-based measures such as Dice coefficient (rank = 7), a standard performance measure in medical image segmentation. Conclusions Average Hausdorff distance-based measures should be used as a standard performance measure in evaluating cerebral vessel segmentation quality. They can identify more relevant segmentation errors, especially in high-quality segmentations. Our findings have the potential to accelerate the validation and development of novel vessel segmentation approaches.

show abstract

“…For example, in ischemic stroke studies, vascular segmentation enables detection and quantification of vascular occlusions, which can be helpful in determining therapeutic options. 1,2 . Structural characteristics can also be used as predictors or markers to assist in the diagnosis of diseases, such as Alzheimer’s disease 3,4 , traumatic brain injury 5 , brain tumours 6 , atherosclerosis 7 , and retinal pathology 8,9 .…”

Section: Background and Summarymentioning

confidence: 99%

MiniVess: A dataset of rodent cerebrovasculature from in vivo multiphoton fluorescence microscopy imaging

Poon

Teikari

Rachmadi

et al. 2022

Preprint

View full text Add to dashboard Cite

We present MiniVess, the first annotated dataset of rodent cerebrovasculature, acquired using two-photon fluorescence microscopy. MiniVess consists of 70 3D image volumes with segmented ground truths. Segmentations were created using traditional image processing operations, a U-Net, and manual proofreading. Code for image preprocessing steps and the U-Net are provided. Supervised machine learning methods have been widely used for automated image processing of biomedical images. While much emphasis has been placed on the development of new network architectures and loss functions, there has been an increased emphasis on the need for publicly available annotated, or segmented, datasets. Annotated datasets are necessary during model training and validation. In particular, datasets that are collected from different labs are necessary to test the generalizability of models. We hope this dataset will be helpful in testing the reliability of machine learning tools for analyzing biomedical images.

show abstract

Automatic segmentation, feature extraction and comparison of healthy and stroke cerebral vasculature

Cited by 37 publications

References 65 publications

Novel imaging markers for altered cerebrovascular morphology in aging, stroke, and Alzheimer's disease

Novel imaging markers for altered cerebrovascular morphology in aging, stroke, and Alzheimer's disease

An evaluation of performance measures for arterial brain vessel segmentation

MiniVess: A dataset of rodent cerebrovasculature from in vivo multiphoton fluorescence microscopy imaging

Contact Info

Product

Resources

About